Corpus ID: 52940653

Model-Free Linear Quadratic Control via Reduction to Expert Prediction

@inproceedings{AbbasiYadkori2019ModelFreeLQ,
title={Model-Free Linear Quadratic Control via Reduction to Expert Prediction},
author={Yasin Abbasi-Yadkori and Nevena Lazic and Csaba Szepesvari},
booktitle={AISTATS},
year={2019}
}
• Published in AISTATS 2019
• Computer Science, Mathematics
Model-free approaches for reinforcement learning (RL) and continuous control find policies based only on past states and rewards, without fitting a model of the system dynamics. They are appealing as they are general purpose and easy to implement; however, they also come with fewer theoretical guarantees than model-based RL. In this work, we present a new model-free algorithm for controlling linear quadratic (LQ) systems, and show that its regret scales as $O(T^{\xi+2/3})$ for any small $\xi>0… Expand Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator • Computer Science, Mathematics • NeurIPS • 2019 A simple adaptive procedure based on$\varepsilon$-greedy exploration which relies on approximate PI as a sub-routine and obtains regret is constructed, improving upon a recent result of Abbasi-Yadkori et al. Expand Learning the model-free linear quadratic regulator via random search • Mathematics, Computer Science • L4DC • 2020 This paper examines the standard infinite-horizon linear quadratic regulator problem for continuous-time systems with unknown state-space parameters and provides theoretical bounds on the convergence rate and sample complexity of a random search method. Expand The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpoint • Mathematics, Computer Science • COLT • 2019 This work shows that for policy evaluation, a simple model-based plugin method requires asymptotically less samples than the classical least-squares temporal difference (LSTD) estimator to reach the same quality of solution; the sample complexity gap between the two methods can be at least a factor of state dimension. Expand Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with √T Regret • Mathematics, Computer Science • ICML • 2021 This work presents the first model-free algorithm that achieves similar regret guarantees, and relies on an efficient policy gradient scheme, and a novel and tighter analysis of the cost of exploration in policy space in this setting. Expand Regret Bound of Adaptive Control in Linear Quadratic Gaussian (LQG) Systems • Computer Science, Mathematics • ArXiv • 2020 The regret upper bound of O(√T) for adaptive control of linear quadratic Gaussian (LQG) systems is proved, where T is the time horizon of the problem. Expand Using Reinforcement Learning for Model-free Linear Quadratic Control with Process and Measurement Noises • Computer Science, Mathematics • 2019 IEEE 58th Conference on Decision and Control (CDC) • 2019 A completely model-free reinforcement learning algorithm to solve the LQ problem where each policy is greedy with respect to all previous value functions and it is proved that the algorithm produces stable policies given that the estimation errors remain small. Expand Average-reward model-free reinforcement learning: a systematic review and literature mapping • Computer Science • ArXiv • 2020 An updated review of work in model-free reinforcement learning is provided and it is extended to cover policy-iteration and function approximation methods (in addition to the value-iterated and tabular counterparts) to identify and discuss opportunities for future work. Expand Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems • Computer Science, Mathematics • AISTATS • 2019 This work characterizes the convergence rate of a canonical stochastic, two-point, derivative-free method for linear-quadratic systems in which the initial state of the system is drawn at random, and shows that for problems with effective dimension$D$, such a method converges to an$\epsilon$-approximate solution within$\widetilde{\mathcal{O}}(D/\ep silon)$steps. Expand Convergence Guarantees of Policy Optimization Methods for Markovian Jump Linear Systems • Computer Science, Mathematics • 2020 American Control Conference (ACC) • 2020 This work proves that the Gauss-Newton method and the natural policy gradient method converge to the optimal state feedback controller for MJLS at a linear rate if initialized at a controller which stabilizes the closed-loop dynamics in the mean square sense. Expand Continuous Control with Contexts, Provably • Computer Science, Mathematics • ArXiv • 2019 This paper studies how to build a decoder for the fundamental continuous control task, linear quadratic regulator (LQR), which can model a wide range of real-world physical environments and presents a simple algorithm, which uses upper confidence bound (UCB) to refine the estimate of the decoder and balance the exploration-exploitation trade-off. Expand References SHOWING 1-10 OF 60 REFERENCES Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems • Computer Science, Mathematics • NIPS • 2012 This work presents an adaptive control scheme that achieves a regret bound of${O}(p \sqrt{T})$, apart from logarithmic factors, and has prominent applications in the emerging area of computational advertising. 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